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A Non-parametric Unsupervised Approach for Content Based Image
Retrieval and Clustering
Technical University
of Crete
Authors:Konstantinos Makantasis (Technical University of Crete) Anastasios Doulamis (Technical University of Crete)Nikolaos Doulamis (National Technical University of Athens)
4th Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams (ARTEMIS 2013)
4TH WORKSHOP ON ANALYSIS AND RETRIEVAL OF TRACKED EVENTS AND MOTION IN IMAGERY STREAMS (ARTEMIS 2013)
OUTLINE
System Overview
Web Query
Image Retrieval
Outliers Removal
Image Clustering
Results
SYSTEM OVERVIEW
Web Query Image Retrieval
Image Clustering
Fully Automatic CBIR4TH WORKSHOP ON ANALYSIS AND RETRIEVAL OF TRACKED EVENTS AND MOTION IN IMAGERY STREAMS (ARTEMIS 2013)
WEB QUERY – IMAGE RETRIEVAL
Query Flickr Image Database
Flickr API in Python
Query keywords are associated with images’ title
Retrieval Retrieved Set: hundreds to
thousands of photos
Retrieved Content: cultural heritage monuments 4TH WORKSHOP ON ANALYSIS AND RETRIEVAL OF TRACKED EVENTS AND MOTION IN IMAGERY STREAMS
(ARTEMIS 2013)
OUTLIERS REMOVAL (1)
Retrieved dataset contains many outliers (visually dissimilar images) Inconsistent human generated
tags
Uninformative machine generated tags
Absence of camera generated meta-data
(Retrieved dataset for “Porta Nigra”)4TH WORKSHOP ON ANALYSIS AND RETRIEVAL OF TRACKED EVENTS AND MOTION IN IMAGERY STREAMS
(ARTEMIS 2013)
OUTLIERS REMOVAL (2)
STEP 1: Visual information encoding SIFT keypoints Each image is
represented by a matrix
STEP 2: Two-way image matching Matches from image to image
Matches from image to image
Final matches between images and
STEP 3: Similarity metric definition For images and
STEP 4: Outliers removal through DBSCAN The goal is to assign to one class
visually similar images and denote visually dissimilar as outliers
However, DBSCAN requires the definition of minimum number of samples per class () and “area” of classes ()
DBSCAN tuning mechanism4TH WORKSHOP ON ANALYSIS AND RETRIEVAL OF TRACKED EVENTS AND MOTION IN IMAGERY STREAMS (ARTEMIS 2013)
OUTLIERS REMOVAL (3)
DBCAN Tuning Mechanism
For a given define function that maps the minimum distance that required for images to have at least neighbors
Find best trade-off point of
Repeat for where and represent the 10% and 90% of retrieved set’s size
Choose and associated with the trade-off point that maximizes “distance from line” 4TH WORKSHOP ON ANALYSIS AND RETRIEVAL OF TRACKED EVENTS AND MOTION IN IMAGERY STREAMS
(ARTEMIS 2013)
OUTLIERS REMOVAL (4)
Visually Similar Images
Outliers
Initial Retrieved
Set
4TH WORKSHOP ON ANALYSIS AND RETRIEVAL OF TRACKED EVENTS AND MOTION IN IMAGERY STREAMS (ARTEMIS 2013)
SPECTRAL CLUSTERING
Similarity Matrix is already computed
Graph where is the set of images and stands for the similarity between them Goal: partition to sub-graphs that contain visually similar images
Random walks on
From compute diagonal degree matrix , with
Compute Laplacian matrix as and normalized Laplacian as
Multiplicity of zero eigenvalues of shown the number of connected components in
Eigengap criterion: set number of clusters for spectral clustering equal to (we used clusters to further eliminate outliers by removing the smallest cluster)
4TH WORKSHOP ON ANALYSIS AND RETRIEVAL OF TRACKED EVENTS AND MOTION IN IMAGERY STREAMS (ARTEMIS 2013)
EXPERIMENTAL RESULTS (1)
Queries for 3 cultural heritage monuments Porta Nigra
Parthenon, Athens
Descobrimentos
Initial Set Outliers Final Set Number of Clusters
Porta Nigra 500 227 (45.4%) 273 (54.6%) 2
Parthenon 500 321 (64.2%) 179 (35.8%) 1
Descobrimentos
500 104 (20.8%) 396 (79.2%) 2
4TH WORKSHOP ON ANALYSIS AND RETRIEVAL OF TRACKED EVENTS AND MOTION IN IMAGERY STREAMS (ARTEMIS 2013)
EXPERIMENTAL RESULTS (2)
Porta Nigra
Initial retrieve
d Set
4TH WORKSHOP ON ANALYSIS AND RETRIEVAL OF TRACKED EVENTS AND MOTION IN IMAGERY STREAMS (ARTEMIS 2013)
EXPERIMENTAL RESULTS (3)
Parthenon
Initial retrieve
d Set
4TH WORKSHOP ON ANALYSIS AND RETRIEVAL OF TRACKED EVENTS AND MOTION IN IMAGERY STREAMS (ARTEMIS 2013)
EXPERIMENTAL RESULTS (4)
Descobrimentos
Initial retrieve
d Set
4TH WORKSHOP ON ANALYSIS AND RETRIEVAL OF TRACKED EVENTS AND MOTION IN IMAGERY STREAMS (ARTEMIS 2013)
CONTRIBUTION
Fully Automatic and Non-parametric algorithm
Handle digital and analog “born” images Handle historic images
Based only on visual information
No a priori knowledge of the dataset
4TH WORKSHOP ON ANALYSIS AND RETRIEVAL OF TRACKED EVENTS AND MOTION IN IMAGERY STREAMS (ARTEMIS 2013)
QUESTIONS
4TH WORKSHOP ON ANALYSIS AND RETRIEVAL OF TRACKED EVENTS AND MOTION IN IMAGERY STREAMS (ARTEMIS 2013)
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